Metamaterial Lens Modeling for mm-wave MIMO Communications | Robust Data Analysis Algorithms in a Neutrino Telescope

Title: Metamaterial Lens Modeling for mm-wave MIMO Communications
by John Brady

The capacity requirements of wireless communications are expanding rapidly with the proliferation of consumer wireless devices. Millimeter wave communication systems are uniquely suited to high data-rate applications not only due to large bandwidths but also small wavelengths that enable exploitation of the spatial dimension. However, state-of-the-art mm-wave systems are unable to take full advantage of these characteristics. Continuous-aperture dish systems achieve large power gain but only support a single data stream. Conversely, conventional multiple-input multiple-output (MIMO) systems support multiple data streams, but suffer from lower power gains. Phased array-based MIMO can, in principle, achieve both multiplexing and power gains through digital beamforming, but is prohibitively expensive and difficult to implement for mm-wave systems due to the complexity of the spatial digital-to-analog (D/A) interface. Continuous Aperture Phased MIMO (CAP-MIMO), achieves the capacity of mm-wave links by combining power and the multiplexing gains, and has a low-complexity D/A interface through the use of a Discrete Lens Array (DLA) that enables analog beamforming. The high-resolution DLA design is based on the Miniaturized Element Frequency Selective Surface (MEFSS) metamaterial. This talk discusses the design and modeling of the DLA in the context of the CAP-MIMO system. The proposed modeling framework provides an insightful, computationally efficient tool for the design and analysis of the DLA.

Title: Robust Data Analysis Algorithms in a Neutrino Telescope.
by Mark Wellons

The Icecube neutrino telescope records the byproducts of interactions between neutrinos and atomic nuclei, and uses this data to reconstruct the trajectory of the neutrino. This is an inherently noisy process, and is complicated by the background radiation generated in the atmosphere. Generating accurate reconstructions requires algorithms robust to noise and outliers, and we show how by using general-purpose robust algorithms and simple models we are able to improved neutrino detection and noise rejection by 9%.